{"id":"W206471768","doi":"","title":"Optimal set recommendations based on regret","year":2009,"lang":"en","type":"article","venue":"Web Personalization and Recommender Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Computer science; Recommender system; Product (mathematics); Set (abstract data type); Heuristic; Semantics (computer science); Expected utility hypothesis; Function (biology); Cartesian product; Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Mathematics; Mathematical economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007411425,0.001249636,0.002830385,0.001786103,0.001156669,0.003050432,0.002535131,0.002447793,0.004071081],"category_scores_gemma":[0.04566788,0.00114841,0.001375505,0.001462928,0.001674604,0.004943675,0.002418094,0.003040985,0.0007247108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002944202,"about_ca_system_score_gemma":0.002009981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004449499,"about_ca_topic_score_gemma":0.003954258,"domain_scores_codex":[0.9905326,0.004779893,0.0005000802,0.001141116,0.0024421,0.000604102],"domain_scores_gemma":[0.9639449,0.02972987,0.001307324,0.002459221,0.001958606,0.0005999899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000315111,0.0001231559,0.001789601,0.0001876084,0.0001651994,0.00008659407,0.0002762418,0.8219953,0.0009412081,0.1020922,0.002840891,0.06918697],"study_design_scores_gemma":[0.00002183041,0.0000414071,0.0001835895,0.00001900822,0.00001797157,0.0000224062,0.0000221766,0.9430143,0.000385171,0.05585996,0.0003971442,0.0000150464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03450347,0.0004599943,0.9588948,0.0006343581,0.00004381348,0.0001157999,0.0001296313,0.0004913008,0.004726795],"genre_scores_gemma":[0.7038357,0.0004536867,0.2909552,0.0003005182,0.0001115738,0.0003881278,0.0004080427,0.0001813552,0.003365708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007411425,"threshold_uncertainty_score":0.03919578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04442913202938047,"score_gpt":0.2855369904126511,"score_spread":0.2411078583832706,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}